Method and Apparatus for Disease Diagnosis and Screening Using Extremely Low Frequency Electromagnetic Fields

ABSTRACT

Novel methods and apparatus for diagnosing or screening disease states in living organisms by the measurement and analysis of extremely low frequency electromagnetic fields, particularly extremely low frequency alternating current. The measurement of such fields is performed at a single point or at several test points on or in the body and compared to one or more reference. Information in the time-varying electromagnetic field is collected, then processed by diagnostic or screening algorithms to provide information about the disease state of the tissue being assessed.

This application claims benefit of priority to U.S. provisional patentapplication No. 61/111,567 filed Nov. 5, 2008 and U.S. provisionalpatent application No. 61/091,100 filed Aug. 22, 2008, each of which ishereby incorporated by reference in its entirety.

TECHNICAL FIELD

Disclosed is a novel method and apparatus for diagnosing or screeningdisease states in living organisms by the measurement and analysis ofextremely low frequency electromagnetic fields. The measurement of suchfields is performed at a single point or at several test points on or inthe body and compared to one or more reference. Information in thetime-varying electromagnetic field is collected, then processed bydiagnostic or screening algorithms to provide information about thedisease state of the tissue being assessed.

BACKGROUND

It is well accepted that the biological activity of organisms, organsystems and cells produces measurable electromagnetic activity. At oneend of the spectrum is the high frequency activity (alternating current)of neural tissue and at the other end is the steady state (directcurrent) activity hypothesized to indicate abnormal cell or tissuegrowth. For example, medical applications of high frequency (AC)electromagnetic field measurements are manifest inelectroencephalographic and electrocardiographic devices. More recently,direct current (DC) fields have been studied as a method of cancerdiagnosis. For example, U.S. Pat. No. 4,328,809 to B. H. Hirschowitz andU.S. Pat. No. 4,955,383 to M. L. Faupel contemplate devices and methodsfor measuring and analyzing DC electropotentials for disease diagnosisor screening. In these inventions, information in the extremely lowfrequency alternating current (AC) band is filtered out throughaveraging a multiplicity of signals taken over time. Higher frequencyinformation is filtered out using active or passive digital or analogfilters. Other manifestations of this approach have been articulated inU.S. Pat. No. 4,407,300 to Davis and U.S. Pat. No. 4,557,273 to Stolleret al. Davis, for example, discloses the diagnosis of cancer bymeasuring the electromotive forces generated between two electrodesapplied to a subject.

If measurements are taken from several test points on the body, ascontemplated in the aforementioned Hirschowitz and Faupel patents, aswell as in U.S. Pat. No. 4,416,288 to Freeman and U.S. Pat. No.4,486,835 to Bai, comparisons of the averaged DC potentials from theplurality of test points may be of particular interest. Furthermore, theaveraged DC voltages may be further analyzed by discriminant functionanalysis, as disclosed in particular by the aforementioned Faupelpatent.

These disease diagnosis techniques using only DC electropotentialssacrifice information (e.g., low frequency AC information) for ease ofprocessing afforded by a singular filtered and/or averaged measurementfrom each test site on the body. Unfortunately, this loss of informationmay compromise diagnostic accuracy of many disease states. For example,public disclosure of clinical studies involving analysis of DCpotentials indicate that while there may be some degree of diagnosticaccuracy for large (palpable) cancers, the same approach appears to berelatively ineffective for small (nonpalpable) cancers. Since it is wellrecognized that early detection of disease states offers the best chancefor patient survival, improvement in this capacity over previousdisclosures is indicated. Moreover, previous pattern recognitiontechniques used for analysis of electropotential fields may be overlysimplistic in that they do not take into account the complexities ofbiological systems and their disease states. To this point, it is knownthat the biology and concomitant electromagnetic activity of malignanttumors, for example, change over time. In order to maximizeeffectiveness, novel diagnostic and screening techniques based on themeasurement of electromagnetic fields must take into account both theshort term changes in electrical activity (e.g., extremely low frequencyAC fields) as well as the longer term changes which occur as a diseasestate progresses. Failure to do so results in major deficiencies leadingto diagnostic inaccuracy.

For example, changes in extremely low frequency alternating current(ELFAC) may differ for malignant vs. benign tumors, because the gatingmechanisms controlling ion transport across the epithelial tissue layercan differ between the diseased and nondiseased condition, or otherreasons. It is known that malignant epithelial cells lose, to varyingdegrees, the ability to transport ions and fluids across the epitheliallayer. It is this low frequency time-varying phenomena which is lost byrestricting analysis of electrical signals to an averaged and/orfiltered DC component.

In addition, changes over the longer term indicate that theelectromagnetic behavior of small malignant tumors may be very differentfrom that of larger tumors, which have been found to produce redoxpotentials as a result of the degradation of tissue within the core ofthe tumor. Another factor is smaller tumors may be more metabolicallyactive and therefore more relatively depolarized than larger tumors.

It follows then that analysis of important electromagnetic changes musttake these factors into account in order to maximize diagnosis andscreening of disease states.

SUMMARY

Disclosed is a novel and improved method and apparatus for integratingelectromagnetic information over time in the form of a set of novelpattern recognition outputs for the diagnosis and screening of diseasestates. The important diagnostic inputs to pattern recognition arereferred to here as extremely low frequency alternating currents(ELFACs). Such method and apparatus operate to measure and analyzeelectromagnetic activity from regions of diseased tissue on or internalto a living organism.

Further disclosed is a novel and improved method and apparatus forpattern recognition which takes into account the biological changeswhich occur as a disease state progresses. This is accomplished byintegrating non-electromagnetic information (e.g., size or area ofdiseased tissue as indicated by an imaging study or palpation of atumor) with the electromagnetic information.

Further disclosed is a novel means for identifying and discriminatingdiagnostically important ELFAC activity from electromagnetic activitydue to noise at the interface between the organic tissue and themeasurement apparatus. This is accomplished by constant measurement ofELFAC activity from the point a measurable signal is achieved from theorganism but recording and analyzing only the portion of the signalindicating that ELFAC activity has achieved a noise-minimized andtherefore diagnostically useful state. Since this is expected to vary ona subject by subject basis, a novel means of determining noise minimizedperformance using pattern recognition is disclosed by the presentinvention.

In some embodiments, the invention provides a novel and improved methodand apparatus for screening an organ system of a living organism for adisease condition. In some embodiments, noise-minimized ELFACs arerecorded from several locations involving an organ system such as theprostate gland or breast. The resultant information is analyzed usingnonlinear pattern recognition techniques to determine if a disease stateexists in an organ system for which no other disease related symptomsare observable. Improvement in screening accuracy may be achieved byintegrating subject variables such as chronologic age or organcharacteristic (e.g., size) information along with ELFAC data into thescreening pattern recognition program.

Still further embodiments of the present invention provide a novel andimproved method for ELFAC diagnosis of malignant disease states. In thisembodiment, recordings of noise-minimized ELFACs from locations on andnear tissue suspected of harboring malignant activity are analyzed usingspecific nonlinear pattern recognition programs which incorporate ELFACdata with known symptomatology of the suspect tissue, such as tumorsize, level of suspicion from imaging studies, age of subject, etc. Thegoal of this approach is to take advantage of the interaction betweenprogressive biological changes which occur during carcinogenesis, tumorgrowth, metastasis and their resultant electromagnetic alterations. Insituations where bilaterally of the organ system is accessible by themeasurement apparatus, as with the breast or extremities, comparison ofthe suspect breast or extremity with the unaffected or opposite breastor extremity can be used to provide a set of internal control measures.Likewise, a control or reference point can be an external solution ofnormal saline with any voltage offset calibrated out of the measurementsystem.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block drawing of the apparatus of some embodiments of thepresent invention;

FIGS. 2 a and 2 b are each a sectional diagram of an electrode for theapparatus of FIG. 1;

FIG. 3 is a flow diagram of the measurement operation of the apparatusof FIG. 1 used to obtain noise-minimized ELFAC signals;

FIG. 4 is a flow diagram representing inputs and outputs of the diseasedecision pattern recognition program.

FIG. 5 is a diagram showing the differentiation between spuriouselectromagnetic activity due to skin/electrode equilibration andnoise-minimized ELFAC signals.

FIG. 6 is a schematic for a passive two-pole band pass filter employedby some embodiments of the invention.

FIG. 7 is a schematic for an active two pole band pass filter employedby some embodiments of the invention.

FIG. 8 is a graph depicting the frequency response of a bandpass filteremployed in some embodiments of the invention.

DETAILED DESCRIPTION

The block diagram for the apparatus in accordance with some embodimentsof the present invention is disclosed in FIG. 1. The apparatus performsthe functions necessary for obtaining and analyzing noise-minimizedELFAC data and integrating those data with other information to producea disease diagnosis. For the purposes of illustration, the apparatus 10will be discussed as configured for the diagnosis of skin cancer,although it should be recognized that the method and apparatus can bereconfigured and similarly employed for screening or diagnosing otherportions or organs of a living human or animal, such as the mammarygland, prostate gland, colon, lung, naseo-pharynx, or other organsystems.

In FIG. 1, a human subject's skin surface 12 may have a suspiciouslesion 14 visible on the right forearm. Clinical inspection of thelesion may be equivocal and a more certain diagnosis may be needed. Inthis case, the location of the lesion is known and a multiple sensorarray 16 is applied to the area of suspicion and a reference sensor isapplied to the mirror image positions on the opposite (left) forearm(not shown). It should be recognized that the opposite forearm is beingused as the reference. In other examples, the reference could be asaline solution or other external reference standard, or an undiseasedportion of the same organ, or other undiseased tissue of the patient,etc. Assume, however, for the purposes of discussion, that the area of alesion 14 is not known, as in the case of screening for breast cancer.In this situation, a large array of electrodes 16 would be used toidentify any area of suspicion occurring on either breast, as indicatedby asymmetries in the ELFAC activities between the two breasts. Thisillustrates that different embodiments of the device and method of thepresent invention contemplate the use of a variety of differentelectrode arrays depending on the specific application, and whether theapplication is for disease screening or diagnosis. Likewise, the numberof recording sensors 16 b may differ depending on the specificapplication. However, in both examples significant disease can bedetected using pattern recognition of ELFAC data in conjunction withother clinical information. Once accessed, analog electromagneticsignals are passed through a bandpass filter 18, converted to digitalform at analog to digital converter 20, processed at CPU 22 using ROM 24and RAM 26, and finally displayed at 28. During processing, the measuredvalue(s) are compared to the reference value(s), and adjusted for othercharacteristics if desired. The result is then compared, in the case ofscreening activity, to the reference to suggest the likelihood ofdisease, and in the case of diagnosis to known patterns to suggest thepossibility and type of disease. The actual steps describing theinvention are subsequently disclosed more specifically in connectionwith FIGS. 2, 3 and 4.

The ELFAC sensing electrodes can be applied either individually or as aset of sensors on an adhesive flexible backing, depending on theapplication. In both cases, effective spacing of electrodes should bemaintained, so that overlap in measurements is minimized. For example,the distance between individual sensors 16 b should be at least twotimes the diameter of the sensor area in contact with the skin or othertissue. If sensors 16 b are applied individually, the technician must betrained to position the sensors using the appropriate distances.Ideally, the electrodes 16 b should be of a type which do not cause asubstantial battery effect due to a dissimilar metals reaction. Mostmodern electrodes have a solid rather than liquid gel, but the solidgels don't penetrate the stratum corneum, so the signal to noise is notoptimal. Another approach is a sensing electrode that consists of asilver or other conductive component 30 having an electrical lead 32connected to the measurement apparatus 10. This electrical connection issecured to the silver component 30 by a conductive top piece 34. A thinlayer of silver chloride 36 is deposited on the surface of the silvercomponent 30. This system is embedded in nonconductive plastic orplastic backing sheets 38. The interface between the organ tissue 12 andthe silver chloride surface 36 is mediated by a semi-liquid electrodecream, paste or gel 40 of a known type such as Synapse electrode crememanufactured by Rose Labs, Inc. The primary goal of the electrode cream40 is to provide a conductive pathway through the electrically resistivecorneal layer of the skin 12. Alternatively, the present inventioncontemplates that the corneal layer of the skin 12 can also be breechedmechanically, either by using recording mini- or micro-electrodes ofknown types (e.g., Beckman Coulter, Inc. Fullerton Calif.) or by usingan array of needle electrodes on a backing sheet which penetrates justbeyond the corneal layer of the skin 12 but are of such small diameternot to cause tissue damage. This alternative electrode design is shownin FIG. 2B at 42 and does not require the use of an electroconductivecream, paste or gel 40. In this embodiment, the conductive component 30contains numerous small penetrating silver or platinum electrodes 42,each of these electrodes could be connected to its own lead whichconnects to the measurement apparatus 10 or summed to one lead (notshown) connected to measurement apparatus 10.

Alternatively, the stratum corneum, which is responsible for more than90% of the electrical resistance of the skin, can be removed bycontrolled use of laser light or a heated wire. In this mode, a laserlight in the near infrared range of the electromagnetic spectrum can beconcentrated on a thin layer of a dye which absorbs the light energy anddissipates the stratum corneum without penetrating to the dermis below.One or more pores of varying size can be produced in this or any othersuitable manner. After the stratum corneum has been selectivelycompromised in this manner, the electrode is placed over the site andELFAC potentials measured. In this instance, the electrode 16 orelectrodes can be placed directly in the pore or pores thus created,without the need for the electrode cream or gel 40, and without the needfor a needle electrode 42.

The measurement device 10 contains multiple recording inputs havingelectrode leads 32 which are affixed to the subject 12. A reference leadis affixed to the patient in a nondiseased area or alternatively to anexternal solution of normal saline or other reference standard. Althoughthe example demonstrates the use of a separate reference electrode, eachrecording electrode could double as a reference electrode for the otherelectrodes. In this embodiment, each recording electrode except one isscanned relative to the remaining electrode. Next, a different“recording” electrode is automatically selected to be the referenceelectrode, and so on until each recording electrode has functioned as areference electrode as well. Since this would result in multiplemeasurements for each recording electrode, the arithmetic average,median or mode could be taken as representative of each recordingchannel.

The measurement apparatus 10 can utilize a selective bandpass filter 18,allowing, for example, the analysis of ELFAC signals in the frequencyrange from 0.01 Hz to 0.1 Hz. Although the present example for cancerdiagnosis may utilize signals within this particular frequency range,the present invention also contemplates using bandpass filters sensitiveto other low frequency ranges, depending on the type of diseasediagnosed or screened for In general, higher ranges may apply to caseswhere there is higher electrical resistance due to skin for example thatmust be bypassed to detect breast cancer. Cancers that occur on thelinings inside the body such as esophageal lung or cervical don't havethis issue and may be closer to true DC (i.e., lower frequency. Untilthe studies are performed we won't have actual numbers. The bandpassfilter 18 may include one or more filters of a known design, asdescribed in the methods for this.

A band pass filter can in principle be constructed by combining a lowand high pass filter in cascade. FIG. 6 shows such a filter. The firstpart (C₁R₁) will pass high frequency signals while the second part(C₂R₂) will pass the low frequencies (or rejects high frequencysignals). However, the filter cannot be considered as a simple cascadingof a high and low pass filter since the second part loads the firstpart. As a result, the overall transfer function is not simply theproduct of the individual transfer functions of the high and low passsections.

The above examples of filters are called passive filters since they donot make use of amplifiers. Among the disadvantages of such a filter isthat there is no gain and that the load resistor R_(L) influences thetransfer characteristic. A better way to build filters for low tomid-frequency applications is to use operational amplifiers. Suchfilters are called active filters. The advantage of active filters isthat one can provide amplification, and have a filter whosecharacteristic is independent of the load.

A simple active band pass filter (two pole system) is shown in FIG. 7.The frequency response (Bode graph) of the gain is given in FIG. 8.

The band pass filter separately filter the signals on each of the inputleads 32 which then pass each of the filtered signals via a separatechannel to a multiple input analog-to-digital converter 20.Alternatively, bandpass filtering could occur in the digital domain postanalog-to-digital conversion. In addition, the bandpass filter 18 couldconstitute an individual filtering mechanism for each channel, providingfiltering only for that channel with each filtered output connected tothe input of the analog-to-digital converter 20.

The analog-to-digital converter 20 ideally should be capable of multipleinput multiplexing such as that manufactured by National Semiconductor,Inc. and designated as ADC808. For very large measurement arrays, suchas those contemplated for breast cancer screening, more than onemultiple input analog-to-digital converter may be necessary, the precisenumber of converters determined by the channel converters' capacity andthe number of channels required for a specific application.

The analog-to-digital converter 20 converts each channel's analog signalto a digital signal which is relayed via a separate output channel tothe multiple inputs of a central processing unit 22. The centralprocessing unit is a component of the larger control system which alsocontains RAM 24 and ROM 26. A stored program in the central processingunit 22 controls signal acquisition and sampling rate, and thenprocesses the digital input data to produce an output to the userregarding the disease state of the tested tissue. Other relevant datasuch as patient age or size of lesion can be inputted by using astandard computer keypad or touch sensitive screen of conventional typeor other input device or method. The central processing unit thenintegrates this information with the ELFAC data using preprogrammedpattern recognition algorithms. The final output to the user is then fedto a display device 28 such as a computer monitor or printer. The outputcould also be stored locally or remotely in a network or other memorysystem. The output can constitute a numerical answer as to theprobability of the disease state existing, a yes/no answer as to whetherthe disease in question exists, or a scalar result indicating theseverity of the disease, and/or a false color image, depending on thespecific application.

The function and operation of the ELFAC device will be understood fromtwo examples of the steps which embody the basic methodology. The firstexample summarizes the method for disease screening while the secondexample characterizes the invention using a diagnostic format.

In the case of screening, the position and disposition of a lesioncannot be identified because the subject is asymptomatic. In this case,a relatively large array of electrodes 16 is positioned either on thesurface 12 of the site in question. If the suspected site can beassessed externally through the skin, then the electrodes may be placedon the skin. If more invasive procedures are indicated, the electrodescan be placed internally at the subject organ or site. In the case ofbreast cancer screening, preferably, the entire surfaces of both breastswould be measured, since the user is unaware if and where a malignancyexists. Once the electrode array 16 is positioned, the referenceelectrode is placed on an undiseased area of tissue or in a referencestandard. Then the ELFAC activity between the reference electrode andeach of the measurement electrodes 16 and is immediately measured,bandpass filtered, and processed by a preprogrammed algorithm whichdetermines if and when diagnostically useful ELFAC readings have beenobtained from a given test subject. At this point, each individualvoltage reading in the waveform is preserved for pattern recognition.This differs from previous techniques in which the apparatus filteredeven low frequency AC information by a combination of active filteringand arithmetic averaging of multiple signals taken over time. Theapproach taken by previous manifestations was to identify only arepresentative direct current (DC) component by utilizing this selectivelow pass filtering and signal averaging. Thus any information in theELFAC component was lost to analysis.

In the case of diagnostic analysis, a sensor 16 is placed at asuspicious site, such as a lesion. The number of electrodes and theirpositioning will depend, in part, on the size of the suspicious site.Generally, the suspicious site and some surrounding tissue should beanalyzed. As with the screening process, a reference electrode is placedat either a reference cite or in a reference standard. A reference sitecould be a corresponding position on a mirror site (e.g. left arm vs.right arm) or an undiseased portion of the same organ or tissue, orother undiseased tissue. ELFAC activity between the reference electrodeand each of the measurement electrodes 16 and treated similarly asdescribed above for screening.

In order to determine whether and when noise is minimized and thereforediagnostically. useful ELFAC signals are obtained from a given subject,monitoring of these signals must be continuous from the time the lastelectrode is placed on the subject until representative ELFAC signalsare obtained. It is well recognized that noise is produced at theskin/electrode interface due to the electrically resistivecharacteristics of the skin's stratum corneum. For skin surfaceelectrodes to be effective transducers of electromagnetic fieldsgenerated by subsurface organ systems, the corneal barrier must bebreeched. However, using even the most highly conductive electrodepastes and gels, achieving signal equilibration takes time; severalminutes or even longer for a given subject. But more problematic is thefact that equilibration time can vary substantially on an individualbasis and can even vary within an individual at different times and/ortest site locations. This is because skin resistance and other factorsrelated to transdermal activity vary on an individual basis. Previouslythe timing and duration of electromagnetic field measurement waspredetermined and the same for each subject essentially built into themachine. This could lead to diagnostic errors because some subjectscould be measured before equilibration was complete. In these cases, itwas noise rather than signal which was measured and analyzed. Thepresent invention circumvents this problem by continuous measurement andmonitoring of ELFAC activity. This is accomplished by analyzing eachincoming signal for stability on an individual basis. For example, thetypical manifestation of uncompleted signal equilibration is aprogressively decreasing (but not increasing) electrical potential seensoon after a measurement electrode is applied and the corneal layer isin the process of being penetrated by the electroconductive paste orgel. Eventually, once equilibration is complete, the informative signalof slowly rising and falling ELFAC potentials may be observed. In someembodiments, the present invention will not start recording andanalyzing electromagnetic signals until the pattern recognition softwarein central processing unit 22 identifies the characteristic ELFACwaveforms which indicate that equilibration has taken place. This meansthat recording time and duration are not preset and the same for eachsubject, but that recording and analysis of electromagnetic activitywill not take place until signal-to-noise is maximized for each subjecttested. Each patient and each procedure therefore is assessedindividually and independently to achieve noise-minimized data in eachtest in each patient. In some embodiments, the signals obtained duringequilibration may be recorded, but not used in the screening ordiagnostic analysis.

As described above, once noise-minimized ELFAC signals are identified,they are recorded and held in RAM memory 26 for analysis. Thepreprogrammed analysis software then employs nonlinear patternrecognition techniques of known types such as artificial neural networksor classification decision trees (e.g., CART). Specifically, allrecorded voltages within the predetermined frequency range are fed intothe pattern recognition program along with optional subject variablessuch as patient age, lesion size, family history of breast cancer (ordisease being screened for, results of imaging studies, etc. The presentinvention contemplates that different pattern recognition programs willbe employed for subsets of subjects based on these and other keyvariables. For example, the biological activity of malignant breasttumors may be different in premenopausal women as opposed topostmenopausal women because of differences in the hormonal milieu insome embodiments, these differences can be taken into account in theanalysis. Small breast cancers which are nonpalpable may be moremetabolically active than larger tumors, whose central core oftenbecomes necrotic. Likewise, metabolic activity of skin lesions may betied to physical appearance, with those lesions appearing as darker,raised mole-like structures more likely to be highly metabolicallyactive.

The flow diagram in FIG. 3 provides an example of inputs, centralprocessing, and the output of a pattern recognition program for thediagnosis of a skin lesion. A start switch 44 begins operation of thecentral processing unit 22, initializing processing operations 46. Theinitializing process brings the various components of the device 10 intooperational mode, including resetting and activation of controlregisters to read data 48 from the analog-to-digital converters 20. Asopposed to prior art devices, the current invention does not initiate apredetermined multiple measurement period at 48. Instead, data is readcontinuously until equilibration is completed at 50 and diagnosticallyuseful ELFACs for each recording channel is identified. The continuousassessment of data at 50 is recycled back through step 48 until all ormost of the channels are determined to carry noise-minimized ELFACsignals.

FIG. 5 represents electromagnetic data as a function of time andamplitude. ELFAC signals digitized at 48 are determined at 50 to beeither obscured by noise 112 or to have reached a noise minimized state114. Only signals which have reached a noise minimized state 114 arerecorded (saved) at 52 and stored for processing at 54.

Should a significant number of channels be determined as unable totransmit noise-minimized ELFAC signals within a reasonable amount oftime (e.g., approximately 15 minutes) the operation would shut down andthe central processing unit 22 would transmit instructions via outputdisplay device 28 for the medical technician to check contact points oremploy other trouble shooting methods. In the case that some channelsare unable to transmit noise-minimize ELFAC signals, the channels thatare able to transmit such signals will be used, provided that the numberof non-transmitting channels is not significant. That is to say that ifsubstantially all the channels are transmitting, the transmittingchannels will be used. By “substantially all” it is meant that at least90% of all channels are able to transmit noise-minimized ELFAC signals.

Once noise-minimized ELFAC signals have been identified, they arecaptured and stored in memory 54. This is in contrast to prior artdevices in which only the averaged DC component is stored in memory forprocessing. The pattern recognition module 58 incorporates nonlinearpattern recognition programs of known type such as artificial neuralnetworks or decision trees which integrate ELFAC data and patientvariables 56 such as patient age, lesion size or shape, level ofsuspicion from imaging studies, etc. to obtain a pattern recognitionresult 60 which leads to a probability statement 62 of whether asuspicious lesion is malignant (as used in a diagnostic mode) or whetheran organ system may harbor an occult malignancy (as used in a screeningmode). The resultant probability statement can be used to indicateCANCER at 64 if it exceeds a certain value, such as 0.05 (5%) or NOCANCER if it does not exceed this value, as in FIG. 4 at 66. Thisindication, or the underlying probability or other interpretation, canbe output to a display device such as a monitor or printer and/or storedin memory. If the present invention is utilized in diagnostic mode, theprobability cut-off point for indicating cancer can be reduced tominimize false negative results (i.e., missed cancers) in symptomaticpopulations with a high a priori prevalence of disease. If, on the otherhand, the present invention is utilized in a screening setting, in whichthe a priori prevalence of disease is relatively low, the process at 76could be calibrated to indicate cancer at a higher probability cut-offpoint. Depending on the specific application, the output to the user at64 or 66 could, in addition to indicating CANCER or NO CANCER, alsoindicate the probability or likelihood that the diagnosis is correct, ora scalar output indicating disease severity. Once the output 64 or 66 isproduced, the program terminates at 68.

An example of a configuration of a pattern recognition module at 58 isgiven in FIG. 4. The pattern recognition program initiates at 70.Subject clinical information, inputted via standard devices at 56 suchas a keypad or from a menu on a touch sensitive screen or any suitableinput device, constitute the initial steps in the sequence. For example,at 72 the program diverges depending on whether the subject is pre- orpost menopausal, if the subject is premenopausal, then the numericalvalue corresponding to day in menstrual cycle is entered at 74. Theprogram diverges again at 76 if the subject is in a hormonally activesegment of the menstrual cycle. This is because disease states are knownto be influenced by systemic hormonal changes. If the subject is in ahormonally active segment, then the pattern recognition program mightweight more heavily differences in point to point ELFAC potentialsbetween the two mirror-image organ systems or locations 78, such asbetween two extremities or between two breasts. If significantdifferences are found at 78, then the PRR value 60 would indicate ahigher probability of cancer, while if these differences are notobserved then waveform characteristics 80 such as frequency, electricalpotential at peak and/or trough of the ELFAC wave are examined todetermine the probability outcome at 82 or 84. If on the other hand thepatient is not in a hormonally active segment of the menstrual cycle,then maximum differences from electrode sites near the area of thesymptoms (e.g., on the same breast or extremity) may be more heavilyweighted by the pattern recognition program at 86. The program may thenproceed to the output probability at 88 if a positive response isachieved, or proceed to 80 for waveform evaluation and subsequentprobability output. The pattern recognition values may differ dependingon whether the input comes from 78 or 86.

Moving now back to 72 in FIG. 4, if the patient is not premenopausal,then it is irrelevant to enter a value corresponding to day in menstrualcycle, as at 74. For the purposes of illustration, the data flow couldproceed on the basis of whether the suspicious lesion being tested ispalpable or not at 92. If more precise size information from imagingstudies is available, these data could also be used. If the lesion isnonpalpable, then the program may proceed to determine if the ELFACwaves indicate general depolarization in the area of the suspiciouslesion (relative to measurements taken from the opposite breast orextremity) at 94 with eventual probability outputs at 96, or 104 or 106via 98. If on the other hand the lesion is palpable at 92, then thepattern recognition program at 100 would weight maximum ELFACdifferences from electrode sites near the area of the symptoms (e.g., onthe same breast or extremity) rather than weighting overalldepolarization relative to the opposite breast or extremity.

If ELFAC differences are above a preset threshold as determined by thepreprogrammed algorithm at 98, then a higher probability of disease isindicated at 106. In each case at 94 and 100, negative outcomes wouldlead to waveform characteristic analysis at 98 with negative outcomesleading to statements of lower probability of disease at 104 andpositive outcomes leading to statements of higher probability of diseaseat 106.

Although FIG. 4 provides one embodiment of a pattern recognitionflowchart, the present invention also contemplates that other biologicalvariables could be added to the pattern recognition module 58. Forexample, lesion palpability 92 could be integrated into the decisionsequence for premenopausal subjects as well. In addition, theprobability statements at 80, 84, 86, 90, 96, 102, 104, and 106 could beintegrated with level of suspicion indexes from other diagnostic studiesor previous ELFAC tests. The number and types of variables would dependon the specific application of disease diagnosis or screening. Forexample, a disease screening pattern recognition module would notinclude lesion characteristics such as palpability or size 92 in FIG. 4.Instead, there would be greater reliance placed on demographic variablessuch as subject age or family history of the disease state beingscreened.

Thus, different decision cut-off points or submodules (e.g., neuralnets, decision trees) such as 78, 82, 88, 94, 98, and 100 in FIG. 4 arerequired for different biological situations in order maximize theeffectiveness of utilizing electromagnetic fields for disease diagnosisand screening.

The method and apparatus of the present invention is designed to providegreater accuracy in diagnosing and screening for disease states usingbiologically tuned pattern recognition of extremely low frequencyalternating current (ELFAC) signals. The signals are measured from anumber of different sites on the body involving a known or suspiciousdisease site. Comparison of the informative aspects of the signals fromthe different sites such as periodicity, peaks, troughs, slopes andother information are integrated with subject variables to provide agreater accuracy in detecting and diagnosing disease states such asepithelial malignancies.

1. A method of screening for or diagnosing a disease condition in a testsubject comprising: obtaining electromagnetic frequency signals betweena test site and a reference site via at least one sensor electrode andat least one reference electrode; band pass filtering the signalsobtained by the sensor electrode to yield sensor ELFAC signals; bandpassfiltering the signals obtained by the reference electrode to yieldreference ELFAC signals; determining likelihood of disease based uponsaid ELFAC signals.
 2. The method of claim 1, wherein said ELFAC signalsare sent to a processing unit for use in the determining step, andwherein said processing unit outputs the determined result to a displaydevice.
 3. The method of claim 1, wherein said determining stepcomprises comparing one or more ELFAC field waveform measurementselected from periodicity, frequency, peaks, troughs, slopes, fractaldimensions, and chaos.
 4. The method of claim 3, wherein said comparingstep further comprises: correlating similar sensor and reference datawith a low risk of disease state; and correlating dissimilar sensor andreference data with a higher risk of disease state.
 5. The method ofclaim 1, wherein the reference electrode is placed on the test subjectat a location complimentary or symmetrical to said test site.
 6. Themethod of claim 1 wherein the reference electrode contacts a referencestandard solution.
 7. The method of claim 1 wherein noise-minimizedELFAC data is used for said determining step, wherein noise-minimizedstatus is established by continuously measuring ELFAC waveforms at saidsensor electrode and said reference electrode during an equilibrationperiod, wherein said equilibration period ending, and noise-minimizeddata being achieved when substantially all of the measured ELFAC fieldsreach equilibrium.
 8. The method of claim 7 whereby the comparing stepis not performed unless and until noise-minimized ELFAC data has beenachieved.
 9. The method of claim 7 wherein noise-minimized ELFAC dataare integrated with user input biological subject variables and comparedusing pattern recognition techniques, and a probability of the presenceof a disease state is determined as a result of the pattern recognition.10. The method of claim 9, wherein said biological subject variables areselected from age of the subject, menopausal stage, day in the menstrualcycle, palpability of a suspicious lesion, size of a suspicious lesion,dimensions of a suspicious lesion, and combinations thereof.
 11. Themethod of claim 9 in which the biological subject variable is themenopausal stage of the subject from which the pattern recognitiontechnique and waveform values to be employed for a given subject aredetermined.
 12. The method of claim 9 in which the biological subjectvariable is the day in the menstrual cycle of the subject from which thepattern recognition technique and waveform values to be employed for agiven subject are determined.
 13. The method of claim 9 in which thebiological subject variable the palpability of a suspicious lesion fromwhich the pattern recognition technique and waveform values to beemployed for a given subject is determined.
 14. The method of claim 9 inwhich the size or dimensions of a suspicious lesion determines thepattern recognition technique and waveform values to be employed for agiven subject.
 15. The method of claim 9 in which the age of the subjectdetermines the pattern recognition technique and waveform values to beemployed for a given subject.
 16. The method of claim 9 which includesdetecting and analyzing ELFAC waveform data in said subject between aleast one location remote from the test site and said reference site andcomparing the relationship between the remote measurement site ELFACdata and ELFAC data measured from at least one sensor electrode locatedat the test site, which includes determining the presence or absence ofa disease state by comparison of said ELFAC data.
 17. The method ofclaim 16 by wherein said sensing electrode(s) at remote location on thesubject is spaced away from the test site.
 18. The method of claim 1whereby electropotential measurements are taken sequentially from one ormore locations on or in the body of a test subject.
 19. The method ofclaim 1 whereby electropotential measurements are taken simultaneouslyor concurrently from one or more locations on or in the body of asubject human or animal.
 20. The method of claim 7, further comprisinggrouping ELFAC signals for each possible sensor electrode/referenceelectrode combination; calculating a measure of central tendency of saidsignals taken from noise-minimized ELFAC measurements; comparing saidmeasures of central tendency for the purpose of disease diagnosis andscreening.
 21. The method of claim 1, further comprising removing of thestratum corneum at least one of said test site and said reference siteby concentrating a laser light on a dye layer placed on the skin surfaceto facilitate contact of the electrode.
 22. An apparatus for diseasedetection or diagnosis at a test site on or in a test subject, saidapparatus comprising: at least one sensor electrode designed for contactwith the test site; at least one reference electrode for contact with areference site; a bandpass filter allowing passage of extremely lowfrequency alternating currents (ELFACs) within the frequency range 0.001Hz to 0.1 Hz; a processing means, configured to collect, analyze andstore data obtained from the electrodes; wherein said sensor electrode,reference electrode are operatively coupled to the processing means viathe band pass filter.
 23. The apparatus of claim 22 whereby thereference electrodes are of the same type as the sensing electrodes andplaced at a location away from the test site on or in the test subject.24. The apparatus of claim 22 whereby the reference electrodes are incontact with a solution of normal saline or other artificial referencematerial.
 25. The apparatus of claim 22, further comprisinganalog-to-digital converter component connected to receive each ELFACsignal from each said sensor electrode and said reference electrode andwherein said processing means is connected to receive each said digitalsignal, said processing means operating to compare said digital ELFACsignals to yield a disease diagnosis or probability indicator.
 26. Theapparatus of claim 22, wherein at least one of said sensor electrodes orsaid reference electrodes comprises an adhesive outer portion whichaffixes the electrode to the surface of the test or reference site, aninner portion further comprising one or more micro- or mini-electrodesof a length capable of penetrating the corneal layer of the skin orouter layer of the organ system at the test site, and wherein eachmicro- or mini-electrode is operatively coupled to the processing means.27. The apparatus of claim 22, wherein said processing means comprisesan equilibration module wherein each ELFAC signal is analyzed forequilibrium, such that only once equilibrium is reached, doe the processperform further analyses.
 28. The apparatus of claim 22, wherein saidprocessing means comprises a pattern recognition module, wherein thesensor ELFAC data is compared to the reference ELFAC data, andcorrelated to the likelihood of a disease state being present.